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from s3prl.corpus.librispeech import librispeech_for_speech2text |
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from s3prl.dataset.speech2phoneme_pipe import Speech2PhonemePipe |
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from s3prl.nn.linear import FrameLevelLinear |
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from s3prl.sampler import FixedBatchSizeBatchSampler, SortedSliceSampler |
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from s3prl.task.speech2text_ctc_task import Speech2TextCTCTask |
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from s3prl.util.configuration import default_cfg |
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from .base import SuperbProblem |
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class SuperbPR(SuperbProblem): |
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@default_cfg( |
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**SuperbProblem.setup.default_except( |
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corpus=dict( |
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CLS=librispeech_for_speech2text, |
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dataset_root="???", |
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), |
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train_datapipe=dict( |
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CLS=Speech2PhonemePipe, |
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), |
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train_sampler=dict( |
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CLS=SortedSliceSampler, |
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batch_size=16, |
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max_length=300000, |
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), |
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valid_datapipe=dict( |
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CLS=Speech2PhonemePipe, |
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), |
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valid_sampler=dict( |
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CLS=FixedBatchSizeBatchSampler, |
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batch_size=8, |
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), |
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test_datapipe=dict( |
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CLS=Speech2PhonemePipe, |
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), |
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test_sampler=dict( |
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CLS=FixedBatchSizeBatchSampler, |
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batch_size=8, |
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), |
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downstream=dict( |
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CLS=FrameLevelLinear, |
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), |
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task=dict( |
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CLS=Speech2TextCTCTask, |
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log_metrics=["per"], |
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), |
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) |
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) |
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@classmethod |
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def setup(cls, **cfg): |
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super().setup(**cfg) |
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@default_cfg( |
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**SuperbProblem.train.default_except( |
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optimizer=dict( |
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CLS="torch.optim.Adam", |
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lr=1.0e-2, |
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), |
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trainer=dict( |
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total_steps=100000, |
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log_step=100, |
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eval_step=1000, |
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save_step=100, |
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gradient_clipping=1.0, |
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gradient_accumulate_steps=2, |
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valid_metric="per", |
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valid_higher_better=False, |
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), |
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) |
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) |
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@classmethod |
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def train(cls, **cfg): |
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super().train(**cfg) |
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@default_cfg(**SuperbProblem.inference.default_cfg) |
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@classmethod |
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def inference(cls, **cfg): |
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super().inference(**cfg) |
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@default_cfg( |
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**SuperbProblem.run.default_except( |
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stages=["setup", "train", "inference"], |
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start_stage="setup", |
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final_stage="inference", |
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setup=setup.default_cfg.deselect("workspace", "resume", "dryrun"), |
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train=train.default_cfg.deselect("workspace", "resume", "dryrun"), |
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inference=inference.default_cfg.deselect("workspace", "resume", "dryrun"), |
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) |
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) |
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@classmethod |
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def run(cls, **cfg): |
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super().run(**cfg) |
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